flutter_edge_ai library

Classes

AssetSource: Asset source - copies from Flutter assets
BundledSource: Bundled source - native platform resources
CanceledError: Download canceled
CancelToken: Token for cancelling model downloads
CorruptionDetectionResult: Result of corruption detection
DiagnosticReport: Diagnostic information for troubleshooting
DownloadError: Download error types using sealed classes for exhaustive pattern matching
DownloadProgress: Progress information for model downloads
EmbeddingBackendProvider: A pluggable embedding backend (LiteRT C API + Gecko/EmbeddingGemma, ONNX, or a third-party backend). Same probe-chain shape as InferenceEngineProvider: selected by probing the embedding model spec, highest-priority first match.
EmbeddingInput: One thing to embed, shaped like Message: optional text plus media.
EmbeddingInstallation: Result of embedding model installation
EmbeddingInstallationBuilder: Fluent builder for embedding model installation
EmbeddingModel: An embedding model, as returned by FlutterEdgeAi.getActiveEmbedder().
EmbeddingModelSpec: Specification for an embedding model: a model file plus its tokenizer (.tflite, .onnx, …), or a single .litertlm bundle that carries its own tokenizer (isSelfContained).
EmbeddingTokenizerProvider: A pluggable source of EmbeddingTokenizerFactory — the "text -> token ids" half of an embedding.
EncodedMediaFormat: An encoded media container (a file format, not a decoded image or waveform).
ErrorHandlingResult: Result of error handling
EstimatedDimensions: Estimated image dimensions
FileSource: File source - external files (mobile only)
FileSystemService: Abstraction for file system operations Allows different implementations for different platforms (mobile/web) and easy mocking in tests
FlutterEdgeAi: Modern API facade for Flutter Edge AI
FlutterEdgeAiDesktop: Desktop implementation of FlutterEdgeAi plugin
FlutterEdgeAiPlugin: Low-level platform-interface tier (the federated-plugin SPI) for flutter_edge_ai. It hosts the abstract runtime types (InferenceModel, InferenceModelSession, InferenceChat, EmbeddingModel, SpeechRecognizer, SpeechSynthesizer, …) and the platform-dispatch singleton instance.
ForbiddenError: HTTP 403 - Access forbidden
FunctionCallParser: Facade for backward compatibility. Delegates to model-specific FunctionCallFormat implementations.
FunctionCallResponse
Gemma3Specs: Gemma 3 SigLIP vision encoder specifications
GeneralVisionSpecs: General vision encoder specifications
HuggingFaceResolver: A pluggable resolver that turns a Hugging Face repo id into a ResolvedHfModel by reading that repo's deployment metadata.
ImageErrorHandler: Comprehensive error handling and debugging utilities for AI image processing to prevent corruption that causes repeating text patterns in model responses.
ImageProcessor: Comprehensive image processing utilities to prevent AI image corruption and ensure proper vision encoder compatibility.
ImageTokenizer: Handles proper image tokenization for multimodal AI models to prevent "Prompt contained 0 image tokens but received 1 images" errors and corruption that causes repeating text patterns.
InferenceChat
InferenceEngineProvider: A pluggable inference engine (MediaPipe .task, LiteRT-LM .litertlm, or a third-party .onnx/.gguf engine).
InferenceInstallation: Result of inference model installation
InferenceInstallationBuilder: Fluent builder for inference model installation
InferenceModel: Represents an LLM model instance.
InferenceModelSession: Session managing response generation from the model.
InferenceModelSpec: Specification for inference models (main model + optional LoRA)
Message
ModelFile: Represents a single file that belongs to a model
ModelFileManager
ModelResponse: Base interface for model responses from InferenceChat Can be either TextResponse, FunctionCallResponse, or ThinkingResponse
ModelRuntimeDefaults: Runtime defaults resolved from a Hugging Face repo's deployment metadata (e.g. a litertlm_manifest.json), in flutter_edge_ai's own runtime vocabulary.
ModelSource: Sealed class representing all possible model sources Provides type-safe alternative to string URLs
ModelSpec: Base specification for any model (inference or embedding)
ModelThinkingFilter
MultimodalImageHandler: Main integration class for handling multimodal image processing in Flutter Edge AI to prevent AI image corruption and repeating text pattern issues.
MultimodalImageResult: Result of multimodal image processing
NetworkError: Network connectivity error
NetworkSource: Network source - downloads from HTTPS/HTTP URLs
NotFoundError: HTTP 404 - Resource not found
OrphanedFileInfo: Information about a potentially orphaned file
ParallelFunctionCallResponse: Multiple function calls in a single model response.
ProcessedImage: Represents a processed image ready for AI model consumption
RateLimitedError: HTTP 429 - Rate limit exceeded
ResolvedHfFile: One file of a multi-file (directory) model resolved from a Hugging Face repo — e.g. an ORT-GenAI model is a DIRECTORY (genai_config.json + model.onnx+`model.onnx_data` + tokenizer files), not a single file.
ResolvedHfModel: A model resolved from a Hugging Face repo's deployment metadata into flutter_edge_ai's install + runtime vocabulary.
ResolvedSampling: Sampling parameters with every field set: what an engine sends.
ResponseValidationResult: Result of response validation
SamplingParams: Sampling parameters for a session, as the caller asked for them.
ServerError: HTTP 5xx - Server error
SessionMetrics: Session metrics containing token usage and performance statistics.
SkillExecutorProvider: A pluggable skill executor — the seam between core and the opt-in flutter_edge_ai_agent package's agentic "skills" runtime.
SpeechRecognizer: Represents a speech-to-text model instance.
SpeechSynthesizer: Represents a text-to-speech model instance.
StorageStats: Storage statistics
SttBackendProvider: A pluggable STT backend (LiteRT C API, or a third-party backend). Same probe-chain shape as EmbeddingBackendProvider/InferenceEngineProvider: selected by probing the STT model spec, highest-priority first match.
SttInstallation: Result of STT model installation
SttInstallationBuilder: Fluent builder for STT (speech-to-text) model installation
SttModelSpec: Specification for STT models (model.tflite + tokenizer.json).
TextResponse: Text token during streaming
ThinkingResponse: Thinking process content from the model
Tool
TtsBackendProvider: A pluggable TTS backend (LiteRT C API, or a third-party backend). Same probe-chain shape as EmbeddingBackendProvider/InferenceEngineProvider: selected by probing the TTS model spec, highest-priority first match.
TtsInstallation: Result of TTS model installation.
TtsInstallationBuilder: Fluent builder for TTS (text-to-speech) model installation.
TtsModelSpec: Specification for a TTS model — a SELECTABLE bundle. ttsModelType carries the model family so one generic backend dispatches to the right runtime profile; sources is one entry per file in that type's manifest.
UnauthorizedError: HTTP 401 - Authentication required
UnknownError: Unknown error
ValidationResult: Result of image validation
VisionEncoderValidator: Validates images for compatibility with AI vision encoders to prevent corruption that causes models to interpret images as repeating text patterns.
VisionSpecs: Base class for vision encoder specifications

Enums

ActivationDataType: Numeric type of the model's activations, for native .litertlm models (Android, iOS, desktop).
CorruptionAction: Actions to take when corruption is detected
EdgeAiLogLevel: Verbosity for flutter_edge_ai's internal logs.
EmbeddingModality: A kind of input an embedding model can turn into a vector.
ErrorType: Types of errors that can occur in image processing
MessageType
ModelFileType
ModelManagementType: Base enumeration for different model management types
ModelReplacePolicy: Policy for what happens to a previously-installed model when a new one is set active. Lives with the spec value types (it's a per-spec install policy); re-exported from model_file_manager_interface.dart for backward compat.
ModelType
PreferredBackend: Hardware backend for model inference.
ResponseAction: Actions to take for corrupted responses
SttModelType: Speech-to-text model families supported by the pluggable STT backends. Only moonshine has a shipped SttModelProfile/pipeline (flutter_edge_ai_speech); the others are follow-ons that need a log-mel frontend.
TaskType: What an embedding is for. Every engine prepends prefix to the TEXT part of an input, native and web alike; media parts never get a prefix.
ToolChoice: Controls whether the model should call tools.
TtsModelType: Text-to-speech model families supported by the pluggable TTS backends. matcha, qwen3 and inflect have shipped TtsModelProfile/pipelines (flutter_edge_ai_speech); kokoro/supertonic are documented follow-ons (fail-loud until wired).
VisionEncoderType: Vision encoder types with their specifications
WebStorageMode: Storage mode for web platform models

Mixins

RawSdkResponseSession: Mixin for sessions that surface the SDK's structured raw JSON response (LiteRT-LM Gemma 4 path with tool_calls). Allows InferenceChat to read the structured tool calls without a hard dependency on a concrete session type, and lets non-FFI sessions opt out by simply not implementing this mixin.

Extensions

DownloadErrorMessage on DownloadError: Extension to convert DownloadError to user-friendly messages

Constants

defaultMaxFunctionBufferLength → const int: Default maximum length for function call buffer before flushing as text. Must accommodate verbose formats (DeepSeek tags, parallel calls).
supportedLoraRanks → const List<int>

Properties

isDesktop → bool: Check if current platform is desktop
no setter

Functions

sniffEncodedMediaFormat(Uint8List bytes) → EncodedMediaFormat?: The container bytes start with, from their magic bytes, or null when they match none of EncodedMediaFormat.known.

Exceptions / Errors

DownloadCancelledException: Exception thrown when a download is cancelled
DownloadException: Custom exception for download failures
EmbedderClosedException: The model was closed, or closed before this request started — so the request never ran.
EmbedderCloseTimeoutException: The previous embedder's close did not finish within the limit (60 s), measured from the moment that close STARTED, so FlutterEdgeAi.getActiveEmbedder built nothing: two native models are never resident at once.
EmbeddingCancelledException: embedAll was cancelled through its CancelToken. No partial results are returned.
EmbeddingEngineException: The engine failed while embedding.
EmbeddingException: Common base of every embedding failure. Not sealed, so a later kind of failure is an additive change; catch this to handle them all.
EmbeddingFormatNotSupportedException: A media part's container is not one the engine accepts (e.g. HEIC where the engine decodes JPEG/PNG/GIF/BMP only).
EmbeddingInputTooLongException: The input is longer than maxInputTokens — task prefix, BOS and EOS included. Nothing was truncated; split the text and embed the pieces.
EmbeddingLoadException: The engine could not load the model, or refused the requested configuration (e.g. a modality it cannot serve).
EmbeddingModalityNotLoadedException: The input carries modality, which this model was not loaded with (loaded is what it was loaded with). Declare media at load: getActiveEmbedder(supportImage: true).
ImageProcessingException: Exception thrown when image processing fails
ImageTokenizationException: Exception thrown when image tokenization fails
ModelStorageException: Exception thrown when model storage operations fail
NoActiveModelError: Thrown when a model is asked for and none is installed and active: the app has to install one first (FlutterEdgeAi.installModel() or installEmbedder()).
UnsupportedEmbeddingCombinationException: The input carries several modalities (requested) and the engine embeds one modality per vector.
VisionEncoderValidationException: Exception thrown when validation fails

flutter_edge_ai library

Classes

Enums

Mixins

Extensions

Constants

Properties

Functions

Exceptions / Errors

flutter_edge_ai package

flutter_edge_ai library